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• W2275441337 abstract "This study addresses some of the shortcomings of traditional soil mapping in providing adequate information about soils for management at property and catchment scales. These limitations relate to the amount of spatial variation in soils that is portrayed by conventional mapping methodologies, as well as the range of soil properties that are generally recorded during soil surveys, and hence are encapsulated in the mapping units. Most current soil mapping programs are based on the soil-landscape paradigm (outlined by Hudson 1992), which assumes that similar soils develop in similar environments. Similar environments or soil zones are delineated through interpretation of imagery, generally aerial photography, with field samples at representative sites being used to characterize the mapping units. At common mapping scales (e.g., 1:50,000), typical sampling densities may be 1 km 2 (Gunn et al. 1998), meaning that considerable interpolation and inference from sample points is involved, and localized variation is not captured. While denser field sampling as part of larger scale surveys may detail more of these variations, the costs are usually prohibitive. In the resultant soil map, the soil mapping units, generally soil classes conforming to an accepted soil classification system, are represented by homogeneous polygons with sharp boundaries. Soil variation within the polygon may be described, but is not spatially represented. Current research within the CRC for Plant Based Management of Dryland Salinity is developing plant growth models and hydrological models as tools for assisting appropriate placement of perennial plants in agricultural landscapes. Both these tools call for higher resolution information about soil variation than is available from existing mapping programs (e.g., PIRSA 2001), and for detail about surface and subsurface attributes that are not well represented in these surveys. These include soil texture and structure, depth of rootzone, depth to impeding layers, and hydraulic conductivity. This project addresses these needs by developing methods for increasing the resolution and spatial variation captured by soil maps, and for prediction of these key subsurface soil properties. STUDY OVERVIEW This study aims to enhance spatial accuracy and precision in mapping surface and subsurface soil attributes. It will adopt a raster approach to soil class and attribute mapping as a means of improving representation of soil variation in the landscape, and explore the use of geophysical data for prediction of soil properties. Recent regolith research has pointed to the potential geophysical methods have for mapping soils, individual soil variables, parent materials and subsurface structures and conditions." @default.
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• W2275441337 date "2003-01-01" @default.
• W2275441337 modified "2023-09-24" @default.
• W2275441337 title "Mapping surface and subsurface soil properties using geophysical remote sensing ansd regolith information" @default.
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